Search: “reasoning”

Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators

arXiv:2605.16575v1 Announce Type: new Abstract: Negotiation requires more than inferring what the other side wants: it requires using that information to make advantageous offers and counteroffers ove

researcharxiv-cs-ai

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CyberCorrect: A Cybernetic Framework for Closed-Loop Self-Correction in Large Language Models

DGX agent

arXiv:2605.17305v1 Announce Type: new Abstract: Large language model (LLM) self-correction -- the ability to detect and fix errors in generated outputs -- remains largely ad hoc, relying on generic pr

researcharxiv-cs-ai

19 May 2026

Deep sequence models tend to memorize geometrically; it is unclear why

arXiv:2510.26745v3 Announce Type: replace-cross Abstract: Deep sequence models are said to store atomic facts predominantly in the form of associative memory: a brute-force lookup of co-occurring enti

DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models

arXiv:2601.11895v3 Announce Type: replace-cross Abstract: DevBench is a telemetry-driven benchmark designed to evaluate Large Language Models (LLMs) on realistic code completion tasks. It includes 1,8

Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control

arXiv:2605.18373v1 Announce Type: cross Abstract: Robotic cloth folding is a challenging task, particularly when considering dynamic folding tasks, which aim at folding cloth by fast motions that leve

researcharxiv-cs-lg

Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders

arXiv:2605.16825v1 Announce Type: cross Abstract: Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the rec

EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL

arXiv:2605.18703v1 Announce Type: new Abstract: Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robus

agentsarxiv-cs-cl

EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.17070v1 Announce Type: new Abstract: While large vision-language models (VLMs) are increasingly adopted as the perceptual backbone for embodied agents, existing benchmarks often rely on que

Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents

arXiv:2605.17625v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into persistent scientific collaborators, context window saturation has emerged as a critical bottleneck. Scienti

EVA01: Unified Native 3D Understanding and Generation via Mixture-of-Transformers

arXiv:2605.16745v1 Announce Type: new Abstract: This paper addresses the challenge of integrating 3D meshes as a native modality within Multimodal Large Language Models (MLLMs). Diffusion-based large

Evaluating Deep Research Agents on Expert Consulting Work: A Benchmark with Verifiers, Rubrics, and Cognitive Traps

arXiv:2605.17554v1 Announce Type: new Abstract: Frontier deep research agents (DRAs) plan a research task, synthesize across documents, and return a structured deliverable on demand. They are being de

EveryQuery: Zero-Shot Clinical Prediction via Task-Conditioned Pretraining over Electronic Health Records

arXiv:2603.07900v2 Announce Type: replace Abstract: Foundation models pretrained on electronic health records (EHR) have demonstrated zero-shot clinical prediction capabilities by generating synthetic

researcharxiv-cs-ai

extit{Don't Guess, Just Ask}: Resolving Ambiguity in Referring Segmentation via Multi-turn Clarification

model-releasesarxiv-cs-cv

arXiv:2605.17531v1 Announce Type: new Abstract: Referring segmentation aims to segment the target objects in images or videos based on the textual query. Despite remarkable progress over the past year

extsc{PrivScope}: Task-scoped Disclosure Control for Hybrid Agentic Systems

arXiv:2605.16630v1 Announce Type: cross Abstract: Hybrid local--cloud agents enrich user requests with context from persistent working state before delegating capability-intensive subtasks to a cloud

Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives

arXiv:2605.17165v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPA) are a promising framework for self-supervised video representation learning, yet the behavior of auxilia

Tutorials

Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation

arXiv:2605.16350v1 Announce Type: cross Abstract: We rethink Federated Learning (FL) from a nested learning perspective, framing the core challenge as how to collaboratively learn optimization rules,

tutorialsarxiv-cs-ai

FIM-LoRA: Task-Informative Rank Allocation for LoRA via Calibration-Time Gradient-Variance Estimation

arXiv:2605.16800v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) assigns a uniform rank to every adapted weight matrix - a practical convenience that ignores a fundamental reality: differe

First Experimental Demonstration of Natural Hovering Extremum Seeking: A New Paradigm in Flapping Flight Physics

arXiv:2508.20836v3 Announce Type: replace Abstract: In this letter, we report the first experimental demonstration of the recently emerged new paradigm in hovering and flapping flight physics called (

researcharxiv-cs-ro

Forecasting Downstream Performance of LLMs With Proxy Metrics

arXiv:2605.18607v1 Announce Type: new Abstract: Progress in language model development is often driven by comparative decisions: which architecture to adopt, which pretraining corpus to use, or which

researcharxiv-cs-cl

FormuLLA: A Large Language Model Approach to Generating Novel 3D Printable Formulations

arXiv:2601.02071v3 Announce Type: replace Abstract: Pharmaceutical three-dimensional (3D) printing is an advanced fabrication technology with the potential to enable truly personalised dosage forms. R

Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps

arXiv:2605.16928v1 Announce Type: cross Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely ei

researcharxiv-cs-ai

GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation

arXiv:2512.23180v3 Announce Type: replace Abstract: Driving World Models (DWMs) have been developing rapidly with the advances of generative models. However, existing DWMs lack 3D scene understanding

safetyarxiv-cs-cv

GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance

arXiv:2605.18252v1 Announce Type: new Abstract: We introduce GaussianZoom, a generative zoom-in 3D reconstruction system with an iterative progressive framework that combines geometry-consistent scene

General Preference Reinforcement Learning

arXiv:2605.18721v1 Announce Type: cross Abstract: Post-training has split large language model (LLM) alignment into two largely disconnected tracks. Online reinforcement learning (RL) with verifiable

Generation Navigator: A State-Aware Agentic Framework for Image Generation

arXiv:2605.17969v1 Announce Type: new Abstract: Despite rapid advances in text-to-image generation, faithfully realizing user intent remains challenging, often requiring manual multi-turn trial and er

safetyarxiv-cs-cv

Geometry-Editable and Appearance-Preserving Object Compositon

arXiv:2505.20914v2 Announce Type: replace Abstract: General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties, while simul

Goal-Conditioned Supervised Learning for LLM Fine-Tuning

arXiv:2605.16345v1 Announce Type: cross Abstract: Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divide

Google just redesigned the search box for the first time in 25 years — here’s why it matters more than you think.

model-releasesventurebeat-ai

For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On T

GraphMind: From Operational Traces to Self-Evolving Workflow Automation

arXiv:2605.17617v1 Announce Type: new Abstract: Complex operational workflows coordinating personnel, tools, and information are central to enterprise operations, yet end-to-end automation remains cha

agentsarxiv-cs-ai

GRASP: Graph Agentic Search over Propositions for Multi-hop Question Answering

arXiv:2605.16598v1 Announce Type: cross Abstract: Agentic retrieval improves multi-hop question answering by giving language models autonomy to iteratively gather evidence. Recent work augments these

agentsarxiv-cs-ai

HAD: Hallucination-Aware Diffusion Priors for 3D Reconstruction

arXiv:2605.16873v1 Announce Type: new Abstract: Diffusion priors have recently demonstrated strong capability in enhancing the quality of sparse-view 3D reconstruction by augmenting training views at

HCLM: A Hierarchical Framework for Cooperative Loco-Manipulation with Dual Quadrupeds

arXiv:2605.17300v1 Announce Type: new Abstract: We introduce HCLM, a hierarchical framework for general-purpose cooperative loco-manipulation with dual quadrupedal systems. Coordinating multi-robot co

safetyarxiv-cs-ro

HEED: Density-Weighted Residual Alignment for Hybrid Vision-Language Model Distillation

arXiv:2605.17093v1 Announce Type: cross Abstract: Distilling vision-language models into faster hybrid architectures, such as 3:1 Mamba-2/attention mixes, is now standard practice for making inference

Helping Customers in Distress: An LLM-powered Agent that Converses, Probes, and Routes

arXiv:2605.16268v1 Announce Type: cross Abstract: Banks receive millions of reports of fraud, scams, and disputed transactions every year, making it challenging to accurately direct customers to the a

Local Ai

Herding CATs: ALARA for Agent Harness Engineering in Portable Composable Multi-Agent Teams

arXiv:2603.20380v2 Announce Type: replace-cross Abstract: Industry practitioners and academic researchers regularly use multi-agent systems to accelerate their work, but the applications through which

local-aiarxiv-cs-ai

How GoPerfect Built an Agentic Recruiting Workforce with Qdrant Cloud

GoPerfect mission is to use an AI recruiting workforce that replaces the manual, low-leverage parts of recruiting. Instead, an agent decomposes recruiter intent and runs the work end to end to find to

agentsqdrant

How Off-Policy Can GRPO Be? Mu-GRPO for Efficient LLM Reinforcement Learning

arXiv:2605.17570v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has been a key driver of recent progress in reinforcement learning with verifiable rewards (RLVR) for large

safetyarxiv-cs-cl

How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning

arXiv:2605.17077v1 Announce Type: cross Abstract: Scaling robot policy learning is bottlenecked by the cost of collecting demonstrations, while language annotations for existing demonstrations are com

HyDRA: Hybrid Dynamic Routing Architecture for Heterogeneous LLM Pools

arXiv:2605.17106v1 Announce Type: new Abstract: Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitude cost differences. Existing routers make binary st

I built a tool that shows you what GPT-2 is 'thinking' in real-time as it generates 3D graph of concept activations per token [R]

researchr-machinelearning

A developer created a visualization tool that displays GPT-2's internal concept activations in real-time during text generation, representing the model's 'thinking' process as a 3D graph that updates

Identifiable Token Correspondence for World Models

arXiv:2605.16457v1 Announce Type: cross Abstract: Transformer-based world models have shown strong performance in visual reinforcement learning, but often suffer from temporal inconsistency in long-ho

InfoFlow: A Framework for Multi-Layer Transformer Analysis

model-releasesarxiv-cs-lg

arXiv:2605.17930v1 Announce Type: new Abstract: While the approximation properties of single-layer Transformer architectures have been studied in recent works, a rigorous theoretical understanding of

Tutorials

Inventorship in AI-Assisted Inventions: Designing an Experiment to Shape Case Law

arXiv:2605.16528v1 Announce Type: cross Abstract: The latest improvements in artificial intelligence (AI) raise new challenges for intellectual property laws, particularly concerning the inventorship

tutorialsarxiv-cs-ai

I've been thinking a lot about the two different groups of evals you need in general agents/agents which handle broad tasks: 1. Benchmark ev…

model-releasesharrison-chase--x

I've been thinking a lot about the two different groups of evals you need in general agents/agents which handle broad tasks: 1. Benchmark evals - this is a suite of up to 100 eval cases which test the

Local Ai

LAST-RAG: Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation for Knowledge-Conditioned Degradation Model Selection

arXiv:2605.17902v1 Announce Type: new Abstract: Stochastic-process-based degradation modeling is a core approach for estimating the distribution of remaining useful life (RUL); however, the selection

local-aiarxiv-cs-ai

Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators

arXiv:2601.20888v3 Announce Type: replace-cross Abstract: We study sampling from posterior distributions in Bayesian linear inverse problems where A, the parameters to observables operator, is computa

researcharxiv-cs-lg

Learning How to Cube

arXiv:2605.16632v1 Announce Type: cross Abstract: Despite the effectiveness of Cube-and-Conquer (C&C) for solving challenging Boolean Satisfiability (SAT) problems, no prior work has shown that transf

Learning over Positive and Negative Edges with Contrastive Message Passing

arXiv:2605.17854v1 Announce Type: new Abstract: Conventional approaches to learning on graphs involve message passing along existing (i.e., positive) edges to update node features. However, these appr

researcharxiv-cs-lg